code: def f_oneway(*args):
      # args = [np.asarray(arg, dtype=float) for arg in args]
      # ANOVA on N groups, each in its own array
      num_groups = len(args)
      alldata = da.concatenate(args)
      bign = len(alldata)
  
      # Determine the mean of the data, and subtract that from all inputs to a
      # variance (via sum_of_sq / sq_of_sum) calculation.  Variance is invariance
      # to a shift in location, and centering all data around zero vastly
      # improves numerical stability.
      offset = alldata.mean()
      alldata -= offset
  
      sstot = _sum_of_squares(alldata) - (
          _square_of_sums(alldata) / float(bign)
      )
      ssbn = 0
      for a in args:
          ssbn += _square_of_sums(a - offset) / float(len(a))
  
      # Naming: variables ending in bn/b are for "between treatments", wn/w are
      # for "within treatments"
      ssbn -= _square_of_sums(alldata) / float(bign)
      sswn = sstot - ssbn
      dfbn = num_groups - 1
      dfwn = bign - num_groups
      msb = ssbn / float(dfbn)
      msw = sswn / float(dfwn)
      f = msb / msw
  
      prob = _fdtrc(dfbn, dfwn, f)  # equivalent to stats.f.sf
  
      return delayed(F_onewayResult, nout=2)(f, prob)
  
imports: from __future__ import annotations
  import math
  from collections import namedtuple
  import numpy
  import dask.array
  from dask import delayed
  from dask.array.ufunc import wrap_elemwise
  from dask.utils import derived_from
  from scipy import special
  from scipy.stats import distributions
label_desc: Use image node_id 11 for calling a global function with example usage: f_oneway() and returns: delayed